Active Infrastructure Sharing – A Key Enabler of Wireless Networks

Innovations and dynamic technological developments motivated the era of Information and Communications. We have witnessed revolution in communication and pattern as well as in the need of connecting world. Telecom industry is always been the core of digitization. Current pandemic situation has reflected new business and connectivity models and huge dependency on the telecom sector. The telecom network enables virtual work and meetings, education, financial transaction, e commerce, health, social meetings, webinars etc. Apart from the common usage for entertainment in terms of OTT, social media – the telecom network has become fundamental to contactless transactions – telemedicine, contactless courier delivery, online shopping etc. This paper presents an overview of telecom infrastructure model, concept of infrastructure sharing and its advantages. It also discusses various factors affecting infrastructure sharing.

COMPUTERIZED TELECOM NETWORK SPARE PARTS MANAGEMENT SYSTEM.

To keep providing network services to subscribers without downtime cause by equipment failure, telecom operators must ensure spare parts are available and are properly managed. No telecom operators can survive without spare parts because it is the life blood for reliable and sustainable network services [1]. The research is focus on designing and developing a web system that will provide adequate information of faulty equipment at telecom base station, will allow online request for spares parts to be done, provide details of transmission media(fiber) faults and capture all faulty equipment due for repairs at the warehouse(technical workshop). Qualitative research approach will be adopted in the study and interview will be used to collect data (users requirements). The automated telecom network spares parts management system will provide efficient access to spares parts, reduce downtime of network services due to equipment failure and boost customers satisfaction.

Social network analysis in Telecom data

Many systems can be represented as networks or graph collections of nodes joined by edges. The social structures in these networks can be investigated using graph theory through a process called social network analysis (SNA). In this paper, networks and SNA concepts were applied using Telecom data such as call detail records (CDRs) and customers data to model our social network and to construct a weighed graph in which each relation carries a different weight, representing how close two subscribers are to each other. In addition, SNA is used to explore the Telecom network and calculate the centrality measures, which help determine the node importance in the network. Depending on centrality measures as well as influence capability of node measure, the influencers in network were detected and targeted by marketing campaigns resulting in 30% raise in growth rate of mobile traffic compared with traditional ways. Finding Multi-SIM subscribers within the same operator or across different operators presents another important concern to Telecom companies because it allows to improve campaigns and churn prediction models. Social network similarity measures and social behavioral measures between nodes were calculated in the Telecom network to detect these Multi-SIM subscribes and 85% accuracy result was achieved for subscribes from different operators and 92% for subscribes from the same operator. The paper is based on a real dataset of 3 months CDRs and customer data provided by a local Telecom operator. This dataset is used to build a network with more than 16 million nodes and more than 300 million edges on a big data platform.